Overview

Dataset statistics

Number of variables14
Number of observations990
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory108.4 KiB
Average record size in memory112.1 B

Variable types

NUM12
BOOL2

Reproduction

Analysis started2020-08-25 02:02:30.794706
Analysis finished2020-08-25 02:02:56.100703
Duration25.31 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

target is uniformly distributed Uniform
Speaker Number has 66 (6.7%) zeros Zeros
target has 90 (9.1%) zeros Zeros

Variables

Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
528
0
462
ValueCountFrequency (%) 
152853.3%
 
046246.7%
 

Speaker Number
Real number (ℝ≥0)

ZEROS

Distinct count15
Unique (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0
Minimum0
Maximum14
Zeros66
Zeros (%)6.7%
Memory size7.9 KiB
2020-08-25T02:02:56.146493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile14
Maximum14
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.322677521
Coefficient of variation (CV)0.6175253601
Kurtosis-1.210766135
Mean7
Median Absolute Deviation (MAD)4
Skewness0
Sum6930
Variance18.68554095
2020-08-25T02:02:56.255656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
14666.7%
 
13666.7%
 
12666.7%
 
11666.7%
 
10666.7%
 
9666.7%
 
8666.7%
 
7666.7%
 
6666.7%
 
5666.7%
 
4666.7%
 
3666.7%
 
2666.7%
 
1666.7%
 
0666.7%
 
ValueCountFrequency (%) 
0666.7%
 
1666.7%
 
2666.7%
 
3666.7%
 
4666.7%
 
5666.7%
 
6666.7%
 
7666.7%
 
8666.7%
 
9666.7%
 
ValueCountFrequency (%) 
14666.7%
 
13666.7%
 
12666.7%
 
11666.7%
 
10666.7%
 
9666.7%
 
8666.7%
 
7666.7%
 
6666.7%
 
5666.7%
 

Sex
Boolean

Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
528
0
462
ValueCountFrequency (%) 
152853.3%
 
046246.7%
 

Feature 0
Real number (ℝ)

Distinct count853
Unique (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.203740404040404
Minimum-5.211
Maximum-0.941
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T02:02:56.371660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-5.211
5-th percentile-4.66925
Q1-3.88775
median-3.1455
Q3-2.6025
95-th percentile-1.7658
Maximum-0.941
Range4.27
Interquartile range (IQR)1.28525

Descriptive statistics

Standard deviation0.868987204
Coefficient of variation (CV)-0.2712414535
Kurtosis-0.4939015008
Mean-3.203740404
Median Absolute Deviation (MAD)0.6325
Skewness0.06639794475
Sum-3171.703
Variance0.7551387607
2020-08-25T02:02:56.472846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-4.03930.3%
 
-3.24230.3%
 
-3.66130.3%
 
-2.97330.3%
 
-4.31630.3%
 
-2.48930.3%
 
-4.05230.3%
 
-4.04730.3%
 
-4.47130.3%
 
-3.06530.3%
 
-3.03430.3%
 
-330.3%
 
-2.44520.2%
 
-2.85920.2%
 
-3.01520.2%
 
-4.09420.2%
 
-4.04520.2%
 
-4.17220.2%
 
-4.08320.2%
 
-2.69620.2%
 
-4.75620.2%
 
-4.04920.2%
 
-3.05520.2%
 
-2.85220.2%
 
-4.2120.2%
 
Other values (828)92893.7%
 
ValueCountFrequency (%) 
-5.21110.1%
 
-5.15810.1%
 
-5.14310.1%
 
-5.13110.1%
 
-5.12510.1%
 
-5.12410.1%
 
-5.10510.1%
 
-5.0810.1%
 
-5.06910.1%
 
-5.05810.1%
 
ValueCountFrequency (%) 
-0.94110.1%
 
-0.96110.1%
 
-1.01210.1%
 
-1.07710.1%
 
-1.09310.1%
 
-1.12310.1%
 
-1.14910.1%
 
-1.20210.1%
 
-1.21510.1%
 
-1.2210.1%
 

Feature 1
Real number (ℝ)

Distinct count877
Unique (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8817636363636365
Minimum-1.274
Maximum5.074
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T02:02:56.859704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.274
5-th percentile-0.09695
Q11.0515
median1.8765
Q32.738
95-th percentile3.7403
Maximum5.074
Range6.348
Interquartile range (IQR)1.6865

Descriptive statistics

Standard deviation1.175272004
Coefficient of variation (CV)0.6245587817
Kurtosis-0.3951885936
Mean1.881763636
Median Absolute Deviation (MAD)0.8435
Skewness-0.0427626963
Sum1862.946
Variance1.381264284
2020-08-25T02:02:56.968323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.0130.3%
 
3.58230.3%
 
1.78430.3%
 
2.14130.3%
 
1.72430.3%
 
1.95230.3%
 
2.09130.3%
 
2.8320.2%
 
2.06720.2%
 
3.22920.2%
 
1.65520.2%
 
1.63320.2%
 
1.92520.2%
 
1.98420.2%
 
1.95420.2%
 
3.18820.2%
 
2.02620.2%
 
1.96820.2%
 
0.11720.2%
 
0.98820.2%
 
3.55720.2%
 
3.34420.2%
 
3.01420.2%
 
1.0920.2%
 
2.02720.2%
 
Other values (852)93394.2%
 
ValueCountFrequency (%) 
-1.27410.1%
 
-1.13710.1%
 
-1.07410.1%
 
-1.02710.1%
 
-0.95610.1%
 
-0.92610.1%
 
-0.92210.1%
 
-0.90410.1%
 
-0.8610.1%
 
-0.82610.1%
 
ValueCountFrequency (%) 
5.07410.1%
 
4.99310.1%
 
4.97610.1%
 
4.84610.1%
 
4.64310.1%
 
4.63710.1%
 
4.56910.1%
 
4.510.1%
 
4.4910.1%
 
4.46110.1%
 

Feature 2
Real number (ℝ)

Distinct count815
Unique (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5077696969696969
Minimum-2.487
Maximum1.431
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T02:02:57.085426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.487
5-th percentile-1.62265
Q1-0.97575
median-0.5725
Q3-0.06875
95-th percentile0.83
Maximum1.431
Range3.918
Interquartile range (IQR)0.907

Descriptive statistics

Standard deviation0.7119482544
Coefficient of variation (CV)-1.402108591
Kurtosis-0.1522731915
Mean-0.507769697
Median Absolute Deviation (MAD)0.444
Skewness0.2355739821
Sum-502.692
Variance0.506870317
2020-08-25T02:02:57.190679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.38950.5%
 
-0.71640.4%
 
-0.81640.4%
 
0.15740.4%
 
-1.59340.4%
 
-0.08830.3%
 
-1.02930.3%
 
-1.1330.3%
 
-0.41530.3%
 
-0.8730.3%
 
-0.82330.3%
 
-0.43330.3%
 
-0.7130.3%
 
-0.3130.3%
 
0.26730.3%
 
-0.89930.3%
 
-1.14230.3%
 
-0.60730.3%
 
-1.00720.2%
 
0.30820.2%
 
-0.80120.2%
 
-0.51420.2%
 
-0.57420.2%
 
-0.73820.2%
 
0.10220.2%
 
Other values (790)91692.5%
 
ValueCountFrequency (%) 
-2.48710.1%
 
-2.43310.1%
 
-2.42310.1%
 
-2.2910.1%
 
-2.26610.1%
 
-2.25410.1%
 
-2.09110.1%
 
-2.0710.1%
 
-2.04110.1%
 
-2.0220.2%
 
ValueCountFrequency (%) 
1.43110.1%
 
1.41310.1%
 
1.38110.1%
 
1.36410.1%
 
1.31210.1%
 
1.28310.1%
 
1.24410.1%
 
1.23110.1%
 
1.21610.1%
 
1.1410.1%
 

Feature 3
Real number (ℝ)

Distinct count836
Unique (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5154828282828282
Minimum-1.409
Maximum2.377
Zeros1
Zeros (%)0.1%
Memory size7.9 KiB
2020-08-25T02:02:57.305054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.409
5-th percentile-0.67485
Q1-0.0655
median0.4335
Q31.096
95-th percentile1.771
Maximum2.377
Range3.786
Interquartile range (IQR)1.1615

Descriptive statistics

Standard deviation0.7592613429
Coefficient of variation (CV)1.472912969
Kurtosis-0.7621395185
Mean0.5154828283
Median Absolute Deviation (MAD)0.559
Skewness0.1289390736
Sum510.328
Variance0.5764777869
2020-08-25T02:02:57.410478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.69940.4%
 
0.14740.4%
 
0.23440.4%
 
-0.36430.3%
 
0.33630.3%
 
-0.01630.3%
 
0.32430.3%
 
-0.15230.3%
 
0.21430.3%
 
0.81430.3%
 
0.30530.3%
 
1.50130.3%
 
1.30730.3%
 
-0.39630.3%
 
0.55530.3%
 
0.30430.3%
 
-0.34330.3%
 
0.93730.3%
 
0.20520.2%
 
0.68520.2%
 
1.50820.2%
 
1.34120.2%
 
0.43920.2%
 
0.3720.2%
 
0.65620.2%
 
Other values (811)91992.8%
 
ValueCountFrequency (%) 
-1.40910.1%
 
-1.33210.1%
 
-1.24710.1%
 
-1.21510.1%
 
-1.19710.1%
 
-1.12710.1%
 
-1.0910.1%
 
-1.04710.1%
 
-1.04410.1%
 
-1.04110.1%
 
ValueCountFrequency (%) 
2.37710.1%
 
2.30710.1%
 
2.30210.1%
 
2.28710.1%
 
2.19110.1%
 
2.16310.1%
 
2.12910.1%
 
2.10610.1%
 
2.03810.1%
 
2.02320.2%
 

Feature 4
Real number (ℝ)

Distinct count803
Unique (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.30565757575757574
Minimum-2.127
Maximum1.831
Zeros2
Zeros (%)0.2%
Memory size7.9 KiB
2020-08-25T02:02:57.531861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.127
5-th percentile-1.38185
Q1-0.769
median-0.299
Q30.1695
95-th percentile0.753
Maximum1.831
Range3.958
Interquartile range (IQR)0.9385

Descriptive statistics

Standard deviation0.6646022606
Coefficient of variation (CV)-2.174335967
Kurtosis-0.2726524234
Mean-0.3056575758
Median Absolute Deviation (MAD)0.47
Skewness0.01649917601
Sum-302.601
Variance0.4416961647
2020-08-25T02:02:57.649294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.04460.6%
 
-0.13140.4%
 
0.12640.4%
 
0.44730.3%
 
-0.34830.3%
 
-0.43630.3%
 
-1.16930.3%
 
-0.69330.3%
 
-0.48430.3%
 
-0.16430.3%
 
0.12730.3%
 
-0.19430.3%
 
-0.07230.3%
 
0.21530.3%
 
-0.15930.3%
 
0.02630.3%
 
-0.31230.3%
 
-0.38630.3%
 
-0.29830.3%
 
-0.61230.3%
 
0.65830.3%
 
-0.37230.3%
 
-1.16430.3%
 
-0.54230.3%
 
-0.01930.3%
 
Other values (778)91091.9%
 
ValueCountFrequency (%) 
-2.12710.1%
 
-2.08810.1%
 
-2.01410.1%
 
-1.99510.1%
 
-1.95310.1%
 
-1.90610.1%
 
-1.90510.1%
 
-1.84110.1%
 
-1.81510.1%
 
-1.76510.1%
 
ValueCountFrequency (%) 
1.83110.1%
 
1.75110.1%
 
1.61810.1%
 
1.5910.1%
 
1.58710.1%
 
1.52910.1%
 
1.11410.1%
 
1.11210.1%
 
1.10610.1%
 
1.10410.1%
 

Feature 5
Real number (ℝ)

Distinct count798
Unique (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6302444444444445
Minimum-0.836
Maximum2.327
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T02:02:57.775869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-0.836
5-th percentile-0.29055
Q10.196
median0.552
Q31.0285
95-th percentile1.72365
Maximum2.327
Range3.163
Interquartile range (IQR)0.8325

Descriptive statistics

Standard deviation0.6038710806
Coefficient of variation (CV)0.9581537543
Kurtosis-0.2863787705
Mean0.6302444444
Median Absolute Deviation (MAD)0.409
Skewness0.3570604221
Sum623.942
Variance0.3646602819
2020-08-25T02:02:57.873248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.04740.4%
 
1.04540.4%
 
0.94140.4%
 
0.38140.4%
 
0.53630.3%
 
1.04430.3%
 
0.78830.3%
 
0.40630.3%
 
0.53230.3%
 
0.4130.3%
 
0.53930.3%
 
0.22630.3%
 
0.17630.3%
 
0.15930.3%
 
0.32930.3%
 
0.28530.3%
 
0.12530.3%
 
0.08130.3%
 
0.7830.3%
 
0.80530.3%
 
0.35230.3%
 
0.28730.3%
 
0.58830.3%
 
0.49830.3%
 
1.12530.3%
 
Other values (773)91192.0%
 
ValueCountFrequency (%) 
-0.83610.1%
 
-0.82110.1%
 
-0.81710.1%
 
-0.80310.1%
 
-0.68710.1%
 
-0.6710.1%
 
-0.66110.1%
 
-0.61710.1%
 
-0.61610.1%
 
-0.54510.1%
 
ValueCountFrequency (%) 
2.32710.1%
 
2.2910.1%
 
2.26810.1%
 
2.26610.1%
 
2.25210.1%
 
2.16910.1%
 
2.10810.1%
 
2.07910.1%
 
2.05910.1%
 
2.04910.1%
 

Feature 6
Real number (ℝ)

Distinct count748
Unique (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0043646464646464695
Minimum-1.537
Maximum1.403
Zeros2
Zeros (%)0.2%
Memory size7.9 KiB
2020-08-25T02:02:57.980480image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.537
5-th percentile-0.79805
Q1-0.307
median0.022
Q30.2965
95-th percentile0.74375
Maximum1.403
Range2.94
Interquartile range (IQR)0.6035

Descriptive statistics

Standard deviation0.4619268382
Coefficient of variation (CV)-105.8337352
Kurtosis0.1524368802
Mean-0.004364646465
Median Absolute Deviation (MAD)0.298
Skewness-0.2061520456
Sum-4.321
Variance0.2133764038
2020-08-25T02:02:58.082540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.19740.4%
 
0.05140.4%
 
-0.31740.4%
 
0.16240.4%
 
0.06740.4%
 
-0.32840.4%
 
0.1240.4%
 
0.04940.4%
 
0.12140.4%
 
0.18740.4%
 
0.14440.4%
 
-0.06140.4%
 
0.19840.4%
 
0.40340.4%
 
0.31740.4%
 
0.08830.3%
 
0.34830.3%
 
0.17430.3%
 
-0.09430.3%
 
0.12730.3%
 
0.53530.3%
 
0.54330.3%
 
-0.13730.3%
 
0.21230.3%
 
0.25230.3%
 
Other values (723)90090.9%
 
ValueCountFrequency (%) 
-1.53710.1%
 
-1.45410.1%
 
-1.44110.1%
 
-1.36310.1%
 
-1.33310.1%
 
-1.28210.1%
 
-1.27510.1%
 
-1.27310.1%
 
-1.23410.1%
 
-1.21910.1%
 
ValueCountFrequency (%) 
1.40310.1%
 
1.28610.1%
 
1.20910.1%
 
1.20210.1%
 
1.17210.1%
 
1.1610.1%
 
1.05910.1%
 
1.01410.1%
 
1.00310.1%
 
0.97510.1%
 

Feature 7
Real number (ℝ)

Distinct count794
Unique (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33655252525252527
Minimum-1.293
Maximum2.039
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T02:02:58.201559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.293
5-th percentile-0.5902
Q1-0.09575
median0.328
Q30.77
95-th percentile1.2901
Maximum2.039
Range3.332
Interquartile range (IQR)0.86575

Descriptive statistics

Standard deviation0.5733020111
Coefficient of variation (CV)1.70345479
Kurtosis-0.4894416536
Mean0.3365525253
Median Absolute Deviation (MAD)0.4325
Skewness0.005957425489
Sum333.187
Variance0.3286751959
2020-08-25T02:02:58.323098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.17550.5%
 
0.50240.4%
 
0.03640.4%
 
-0.46830.3%
 
0.27230.3%
 
-0.4530.3%
 
0.86230.3%
 
0.76130.3%
 
0.71630.3%
 
-0.23130.3%
 
0.33630.3%
 
0.67630.3%
 
0.17230.3%
 
0.61330.3%
 
-0.28230.3%
 
0.30530.3%
 
0.72330.3%
 
0.70230.3%
 
0.8230.3%
 
0.69330.3%
 
0.83230.3%
 
-0.13730.3%
 
-0.04730.3%
 
-0.01630.3%
 
0.27430.3%
 
Other values (769)91192.0%
 
ValueCountFrequency (%) 
-1.29310.1%
 
-1.21110.1%
 
-1.19110.1%
 
-1.08210.1%
 
-1.07910.1%
 
-0.98210.1%
 
-0.94910.1%
 
-0.94710.1%
 
-0.91610.1%
 
-0.88710.1%
 
ValueCountFrequency (%) 
2.03910.1%
 
1.97210.1%
 
1.93910.1%
 
1.7710.1%
 
1.67410.1%
 
1.67310.1%
 
1.66710.1%
 
1.61410.1%
 
1.59710.1%
 
1.58310.1%
 

Feature 8
Real number (ℝ)

Distinct count788
Unique (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.3029757575757576
Minimum-1.6130000000000002
Maximum1.3090000000000002
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T02:02:58.448448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.613
5-th percentile-1.2702
Q1-0.704
median-0.3025
Q30.09375
95-th percentile0.62065
Maximum1.309
Range2.922
Interquartile range (IQR)0.79775

Descriptive statistics

Standard deviation0.570161616
Coefficient of variation (CV)-1.88187207
Kurtosis-0.4570102014
Mean-0.3029757576
Median Absolute Deviation (MAD)0.4015
Skewness0.05386976153
Sum-299.946
Variance0.3250842684
2020-08-25T02:02:58.555358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.38740.4%
 
-0.28340.4%
 
0.02340.4%
 
-0.35840.4%
 
-0.67740.4%
 
0.0530.3%
 
-0.16230.3%
 
-0.72130.3%
 
-0.330.3%
 
-0.30730.3%
 
-0.16530.3%
 
-0.26830.3%
 
-0.27730.3%
 
-0.61430.3%
 
-0.5430.3%
 
0.12330.3%
 
0.03330.3%
 
-0.68730.3%
 
-0.17830.3%
 
-0.57730.3%
 
-0.80930.3%
 
-0.54130.3%
 
-0.87430.3%
 
-0.61130.3%
 
-0.47330.3%
 
Other values (763)91091.9%
 
ValueCountFrequency (%) 
-1.61310.1%
 
-1.60910.1%
 
-1.56910.1%
 
-1.55810.1%
 
-1.55510.1%
 
-1.53810.1%
 
-1.52110.1%
 
-1.5210.1%
 
-1.51510.1%
 
-1.50710.1%
 
ValueCountFrequency (%) 
1.30910.1%
 
1.20610.1%
 
1.12710.1%
 
1.10310.1%
 
1.07410.1%
 
1.07310.1%
 
1.05810.1%
 
1.04510.1%
 
1.0210.1%
 
1.00710.1%
 

Feature 9
Real number (ℝ)

Distinct count775
Unique (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.07133939393939392
Minimum-1.68
Maximum1.396
Zeros1
Zeros (%)0.1%
Memory size7.9 KiB
2020-08-25T02:02:58.671647image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.68
5-th percentile-0.9361
Q1-0.548
median-0.1565
Q30.371
95-th percentile0.995
Maximum1.396
Range3.076
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.6039854976
Coefficient of variation (CV)-8.466367097
Kurtosis-0.7577231771
Mean-0.07133939394
Median Absolute Deviation (MAD)0.4405
Skewness0.2957696848
Sum-70.626
Variance0.3647984813
2020-08-25T02:02:58.780124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.30150.5%
 
0.16840.4%
 
0.4140.4%
 
-0.34340.4%
 
-0.26740.4%
 
-0.45640.4%
 
0.1930.3%
 
-0.39130.3%
 
-0.41330.3%
 
-0.52530.3%
 
-0.56430.3%
 
-0.31830.3%
 
-0.32230.3%
 
-0.56230.3%
 
-0.50730.3%
 
-0.10930.3%
 
-0.07430.3%
 
0.19830.3%
 
-0.54830.3%
 
-0.25630.3%
 
-0.76430.3%
 
-0.11830.3%
 
-0.0630.3%
 
-0.2530.3%
 
-0.25920.2%
 
Other values (750)90991.8%
 
ValueCountFrequency (%) 
-1.6810.1%
 
-1.51810.1%
 
-1.48410.1%
 
-1.39910.1%
 
-1.3210.1%
 
-1.29710.1%
 
-1.24110.1%
 
-1.22610.1%
 
-1.20510.1%
 
-1.19710.1%
 
ValueCountFrequency (%) 
1.39610.1%
 
1.29410.1%
 
1.29110.1%
 
1.24510.1%
 
1.21410.1%
 
1.20610.1%
 
1.19310.1%
 
1.18410.1%
 
1.1810.1%
 
1.17710.1%
 

target
Real number (ℝ≥0)

UNIFORM
ZEROS

Distinct count11
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0
Minimum0
Maximum10
Zeros90
Zeros (%)9.1%
Memory size7.9 KiB
2020-08-25T02:02:58.896615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.163875981
Coefficient of variation (CV)0.6327751962
Kurtosis-1.220098918
Mean5
Median Absolute Deviation (MAD)3
Skewness0
Sum4950
Variance10.01011122
2020-08-25T02:02:58.997118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10909.1%
 
9909.1%
 
8909.1%
 
7909.1%
 
6909.1%
 
5909.1%
 
4909.1%
 
3909.1%
 
2909.1%
 
1909.1%
 
0909.1%
 
ValueCountFrequency (%) 
0909.1%
 
1909.1%
 
2909.1%
 
3909.1%
 
4909.1%
 
5909.1%
 
6909.1%
 
7909.1%
 
8909.1%
 
9909.1%
 
ValueCountFrequency (%) 
10909.1%
 
9909.1%
 
8909.1%
 
7909.1%
 
6909.1%
 
5909.1%
 
4909.1%
 
3909.1%
 
2909.1%
 
1909.1%
 

Interactions

2020-08-25T02:02:31.461115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:31.626866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:31.782523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:31.940241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:32.110791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:32.281734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:32.458228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:32.616716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:32.782161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:32.947054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:33.107579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:02:33.440841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:33.600838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:02:37.852565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:38.029042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:02:42.847995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:43.015031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:02:44.671843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:44.820465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:44.972654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:45.126649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:45.277106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:02:47.544059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:02:48.026126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:48.192410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:48.357293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:02:49.331118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:49.486614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:49.644094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:49.802500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:49.946538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:50.099121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:02:50.427459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:50.589591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:50.742177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:50.892119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:51.045266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:51.203715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:51.360244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:51.510585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:51.877640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:52.033169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:52.206765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:52.371074image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:52.538173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:52.707579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:52.866096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:53.027730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:53.188931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:53.343075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:53.503710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:53.659406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:53.818385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:53.964180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:54.122723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:54.289313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:54.449044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:54.606056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:54.761941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:54.913455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:55.062285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:55.210760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:55.361989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T02:02:59.131717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T02:02:59.400749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T02:02:59.669557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T02:02:59.942389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T02:02:55.624116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:55.951308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

Train or TestSpeaker NumberSexFeature 0Feature 1Feature 2Feature 3Feature 4Feature 5Feature 6Feature 7Feature 8Feature 9target
0101-3.6390.418-0.6701.779-0.1681.627-0.3880.529-0.874-0.8148
1101-3.3270.496-0.6941.365-0.2651.933-0.3630.510-0.621-0.4882
2101-2.1200.894-1.5760.147-0.7071.559-0.5790.676-0.809-0.0491
3101-2.2871.809-1.4981.012-1.0531.060-0.5670.235-0.091-0.7950
4101-2.5981.938-0.8461.062-1.6330.7640.394-0.1500.277-0.3965
5101-2.8521.914-0.7550.825-1.5880.8550.217-0.2460.238-0.3656
6101-3.4822.524-0.4331.048-1.9950.9020.3220.4500.377-0.3663
7101-3.9412.3050.1241.771-1.8150.593-0.4350.9920.575-0.3019
8101-3.8602.116-0.9390.688-0.6751.679-0.5120.928-0.167-0.4344
9101-3.6481.812-1.3781.5780.0651.577-0.4660.7020.060-0.83610

Last rows

Train or TestSpeaker NumberSexFeature 0Feature 1Feature 2Feature 3Feature 4Feature 5Feature 6Feature 7Feature 8Feature 9target
9800140-3.6201.0660.1581.3930.8791.020-0.0900.087-1.3330.5852
9810140-3.4681.810-0.3901.550-0.084-0.008-0.482-0.231-0.7240.9671
9820140-2.7951.957-1.2590.790-0.2740.078-0.1140.005-0.3091.1420
9830140-3.0022.944-1.379-0.188-0.1310.9570.346-0.392-0.7110.6175
9840140-2.9242.731-1.1380.0660.1000.6830.162-0.399-0.8820.8276
9850140-3.2393.083-1.427-0.202-0.2821.4210.5760.068-0.9140.1473
9860140-3.7533.605-0.899-0.747-0.4011.7650.6200.754-0.835-0.3019
9870140-3.9802.4590.0680.0230.2371.029-0.1890.521-0.773-0.5004
9880140-4.2642.9250.0650.7940.3230.515-1.282-0.140-0.863-0.39010
9890140-3.2912.324-0.6790.2850.4410.557-0.2270.115-1.0460.6977